Method and system for optimizing optical inspection of patterned structures

ABSTRACT

A system and method are presented for use in inspection of patterned structures. The system comprises: data input utility for receiving first type of data indicative of image data on at least a part of the patterned structure, and data processing and analyzing utility configured and operable for analyzing the image data, and determining a geometrical model for at least one feature of a pattern in said structure, and using said geometrical model for determining an optical model for second type of data indicative of optical measurements on a patterned structure.

FIELD OF THE INVENTION

This invention is generally in the filed of optical inspection, andrelates to a method and system for optimizing optical inspection ofpatterned structures.

BACKGROUND OF THE INVENTION

Semiconductor structures, such as integrated circuits, become morecomplicated in the dimensions and shapes of pattern features.Accordingly, there exists an increasing need in providing accuratemeasurements of full 3-dimensional structures, and in enabling thesemeasurements to be applied to structures progressing on a productionline, i.e. automatic inspection (metrology, defect detection, processcontrol, etc.) of patterned structures.

Current metrology techniques heavily rely on test structures (which aretypically produced in the scribe lines of a wafer) and on attempt torepresent the process behavior inside the structure (which is not alwayssuccessful). However, measuring directly the features inside thestructure has a significant benefit as it allows both the relevance thattest structures sometimes lack and the ability to map the changes acrossthe structure.

Currently several metrology methods exist for the measurement of2-dimensional (lines) and 3-dimensional structures. These methods can beroughly divided into four main groups including optical imagingtechniques, beam scanning 20 techniques, probe-based microscopy, and“non-imaging” optical techniques, usually termed scatterometry oroptical critical dimension (OCD) measurement.

Optical imaging techniques are based on creation of a direct image ofthe region (area) of a sample. These techniques are in most cases nolonger relevant for accurate geometrical measurements of such patternedstructures as semiconductor wafers, because the features size of apattern is much smaller than the wavelength used for imaging. Thislimitation may sometimes be overcome by utilizing aerial image of a masktaken prior to magnification down to the wafer, as done in someinspection tools (steppers).

Beam scanning techniques are based on scanning a given area of a samplewith a focused beam of particles, collecting any kind of radiationproduced by interaction between the beam and the sample (usuallysecondary particle emission), and using intensity (or other parameter)of the collected radiation to create a 2-dimensional image of thesample. Such techniques include, for example, SEM (Scanning ElectronMicroscopy) and Hellium Ion Beam Microscopy.

Probe-based microscopy utilizes a probe (tip) which is brought in closevicinity with the sample (such as for example in AFM—Atomic ForceMicroscopy) and scans a line or an area of the sample. Signals from theprobe or, more often, feedback control signals (which are used to keepthe probe at a constant distance from the sample) are used for creationof a 2-dimensional image of the sample.

Scatterometry or OCD techniques are based on measurement of diffractionfrom a repetitive structure on a sample (grating), having periodicity ineither one or two directions, and reconstruction of the geometricalparameters of a unit cell of a pattern through solving the inverseproblem, and fitting a model to the measurement results. Here, ameasurement spot contains many periods of the repetitive structure,hence a measurement represents the average parameters across themeasurement spot.

It should be noted that optical imaging techniques, beam scanningtechniques and probe-based microscopy can all be implemented as scanningtechniques and can create an image by scanning probe with highresolution (i.e. sensitive to a small part of the sample at a time) overthe sample. As for the scatterometry or OCD techniques, they haveseveral advantages, such as high speed and repeatability, but theyusually suffer from a significant handicap which is the long setup timerequired before a measurement can be performed. This issue is moresevere in case of 3-dimensional structures as they become more complex,because the number of parameters becomes larger and the diffractioncalculation becomes longer.

One of the known approaches to circumvent the above issues is bycombining information from additional sources, such as CD-SEM or AFM,for example as described in U.S. Pat. No. 6,650,424 assigned to theassignee of the present application. According to this techniquescatterometry and SEM measurements are applied to a structure, andmeasured data indicative of, respectively, the structure parameters andlateral pattern dimensions of the structure are generated. The entiremeasured data are analyzed to enable using measurement results of thescatterometry for optimizing the measurement results of SEM and viceversa.

GENERAL DESCRIPTION

There is a need in the art for facilitating inspection of patternedstructures, including complex structures having a complexthree-dimensional pattern.

It should be understood that for the purposes of this patentapplication, the term “inspection” should be interpreted broadly,including measurements, metrology and/or defect detection and/or processcontrol/monitoring, etc. Also, in the description below, “imaging”scanning techniques, such as SEM, Hellium Ion Beam Microscopy, and AFM,are referred to as a scanning technique or system or tool, or metrologytechnique or system or tool, and should be distinguished from“non-imaging” technique such as scatterometry or OCD.

The present invention provides for optimizing creation of an opticalmodel for describing/interpreting OCD measured data. In this connection,it should be understood that optical models are typically used forinterpretation of optical measurements. Such optical model includes oneor more functional representations of a dependence of an opticalresponse from a structure on one or more structure-relatedparameters/conditions and parameters/conditions of a measurement system.

The present invention optimizes the optical model creation by optimizingcreation of a geometrical model of the structure under measurements(being part of structure related data), on which the optical model isbased. The present invention utilizes information (measured data) fromany metrology tool of the kind providing (directly or indirectly) imagedata or bitmap of a sample to optimize modeling of OCD measurements,which reduces the setup time for complex 3-dimensional patternedstructure and provides more accurate measurements. The image data may beprovided using measurements from a scanning tool (e.g. AFM, SEM, etc.).It should be noted that this optimization technique may be carried outoff line or on line (real time), or a combined approach may be used.

In OCD techniques, one of the key steps is mathematical/theoreticalrepresentation of the geometry of a unit cell of a pattern in a mannerthat allows creation of a physical model (e.g. based on the principlesof RCWA) for further interpretation of actual measured data. In mostcases, the process of determination of the geometry of the unit cell iscarried out during s recipe setup for each patterned layer in thestructure. This is typically performed as follows: A user selects one ormore unit cell geometries (geometrical models) from a given set ofsimple shapes (so-called “primitives”), adjusts the parameters of theselected primitive, and changes at least some of these parameters, e.g.center position and/or dimensions thereof. These steps might berepeated, if needed, until the unit cell can be sufficiently describedfor the physical model creation. Then, during the physical modelcreation/calculation based on the defined unit cell, suitable algorithmsare used for performing the following: The current geometricalparameters are used to define the shape; discretization (slicing) isapplied in a vertical (z) direction by slicing the features into severalartificial layers, each containing a slightly different shape, and inlateral (x, y) directions; and the resulting, discretized structure isused to calculate a desired function (e.g. RCWA), such aselectromagnetic response of a patterned structure, e.g. spectralresponse, diffraction pattern, etc.

However, the above process or similar processes currently used, sufferfrom the fact that the use of simple geometrical primitives does notallow to accurately describe the real geometry of the unit cell on thereal structure (e.g. wafer). Additionally, the above process, apart frombeing in some cases time consuming, tends to produce a large set ofparameters that are supposedly independent, while being in realitystrongly correlated through the process behavior, e.g. size in xdimension and size in y dimension.

The present invention, according to its one aspect, provides a newapproach, a so-called “hybrid approach” for optimizing OCD modelingbased on data provided by imaging technique. As indicated above, animaging tool is at times referred to herein as a scanning tool or ametrology tool. An example of the technique of the present invention isthe use of data from a CD-SEM (metrology tool) for optimizing modelingof or measurements by an OCD, Scatterometry tool resulting in enhancedperformance of either one or both of the metrology and CD measurementsthat cannot be obtained separately.

Thus, according to one broad aspect of the invention, there is provideda system for use in inspection of patterned structures, the systemcomprising: data input utility for receiving first type of dataindicative of image data of at least a part of the patterned structure,and data processing and analyzing utility configured and operable foranalyzing the image data, and determining a geometrical model for atleast one feature of a pattern in said structure, and using saidgeometrical model for determining an optical model for second type ofdata indicative of optical measurements on a patterned structure.

In some embodiments of the invention, the data processing and analyzingutility comprises an identifier utility configured and operable forprocessing data indicative of said image data and determining a contourfor at least one feature of the pattern, and a geometrical model creatorutility connected to said identifier utility and operable for thedetermination of the geometrical model.

The data processing and analyzing utility may comprise an identifierutility configured and operable for processing the image data andidentifying at least one unit cell comprising said at least one featureof the pattern, and generating said data to the contour identifierutility.

The system may also comprise a memory utility. The memory utility mayserve for storing certain design rule data indicative of at least onefeature of a pattern in said structure.

In some embodiments, the data processing and analyzing utility isconfigured and operable for receiving measured data of said second typeand processing it for optimizing the first image data.

The image data may include measured data obtained by a scanning tool.The scanning tool may include a SEM and/or AFM tool.

In some embodiments, the second type of data corresponds to measureddata obtainable by a scatterometry based tool.

According to another broad aspect of the invention, there is provided ameasurement system comprising at least one measurement tool forobtaining measured data of at least one of the first and second types,and the above described system for the optical model creation configuredfor communicating with said at least one measurement tool.

According to yet another aspect of the invention, there is provided ascatterometry system comprising a measurement tool configured andoperable for measuring on patterned structures and generating opticaldata of a second type, and the above-described system for the opticalmodel creation.

According to yet further aspect of the invention, there is provided amethod for use in inspection of patterned structures, the methodcomprising: receiving first type of data indicative of image data on atleast a part of the patterned structure, processing and analyzing dataindicative of the image data and determining a geometrical model for atleast one feature of a pattern in said structure, using said geometricalmodel for determining an optical model for second type of dataindicative of optical measurements on a patterned structure.

The determination of the geometrical model may comprise processing andanalyzing data indicative of the received image data and determining acontour for at least one feature of the pattern, and processing said atleast one contour for the determination of the geometrical model. Thereceived image data may be first processed and analyzed for identifyingat least one unit cell comprising said at least one feature of thepattern. To this end, certain design rule may be utilized providing dataindicative of at least one feature of a pattern in said structure.

The method may operate for receiving measured data of said second typeand processing it for optimizing the first image data.

The image data may include measured data obtained by a scanning tool.The latter may include SEM and/or AFM.

The second type of data may correspond to measured data obtainable by ascatterometry based tool.

The method may be used for inspection of semiconductor wafers.

According to yet another aspect of the invention, there is provided amethod for use in inspection of patterned structures, the methodcomprising: receiving image data indicative of one or more images of atleast a part of the patterned structure obtained by a scanning tool,processing and analyzing data indicative of said image data anddetermining a geometrical model for at least one feature of a pattern insaid structure, using said geometrical model for determining an opticalmodel for scatterometry based optical measurements on a patternedstructure, thereby enabling use of said geometrical model forinterpreting scatterometry based measurements applied to the patternedstructure progressing on a production line.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to understand the invention and to see how it may be carriedout in practice, embodiments will now be described, by way ofnon-limiting example only, with reference to the accompanying drawings,in which:

FIG. 1 is a schematic illustration of a simplified example of a unitcell suitable for OCD technique applied to patterned structures;

FIG. 2A exemplifies a system of the present invention for creation of anoptical model for interpretation of optical measurements, e.g. OCD, onpatterned structures based on image data provided by an imaging tool(metrology tool), for example SEM;

FIG. 2B exemplifies a method of the present invention for optical modelcreation based on identification of the unit cell from an image dataobtained by a scanning tool, such as SEM;

FIG. 3 exemplifies the principles of a process of stretching/shrinkingthe contour of a feature in the unit cell for creation of geometricalmodel used in the method of FIG. 2B;

FIG. 4 exemplifies how the present invention is used for utilizingreal-time OCD measurements for further optimizing the optical modelpreviously created during an initial off line stage;

FIGS. 5 and 6 illustrate schematically how the principles of the presentinvention can be used for combining data from two measurement tools ofdifferent types for optimizing the optical model for use ininterpretation of measured data from at least one of these tools: FIG. 5illustrates an example where correlation parameters are found off linefrom SEM tool data and OCD tool data, and FIG. 6 shows an example wherecorrelation parameters are obtained during a setup stage (e.g. from SEMdata) and are then used to optimize real-time measurements (e.g. OCDmeasured data); and

FIG. 7 exemplifies a method of the invention for jointly optimizing datainterpretation models of measurement tools of different types usingmeasured data from both of these tools, by creating a combined model forboth SEM and OCD measurements.

DETAILED DESCRIPTION OF EMBODIMENTS

Reference is made to FIG. 1, illustrating a simplified example of a unitcell, generally designated 10, suitable for OCD technique applied topatterned structures. As shown, the unit cell 10 includes a patternformed by spaced-apart features of different geometries (shapes anddimensions). In this specific but not limiting example, the featuresinclude three bars (lines) L₁, L₂ and L₃ characterized by the line width(critical dimension CD₁) and a space between the lines (criticaldimension CD₂), two cylindrical elements (pins) P₁ and P₂ of a certaindiameter (critical dimension CD₃), and an L-shaped feature Scharacterized by critical dimensions CD₄ and CD₅. It should beunderstood that the unit cell may have any other configuration, and theunit cell shown in the figure would practically be characterized byadditional parameters, such as side wall angle (SWA), the line widthprofile, height, etc.

FIGS. 2A and 2B exemplify system and method respectively of the presentinvention for creation of an optical model for interpretation of opticalmeasurements on patterned structures based on image data provided by animaging tool (metrology tool), for example SEM. This modeling processprovides for utilizing image data corresponding to a 2-dimensional imageobtained by the imaging tool to optimize operation of an OCD tool. Itshould be noted that the modeling may be carried out on line (in realtime) by directly processing image data from the imaging tool, or offline utilizing image data from a storage device. As for the OCDoptimization procedure, it also may be either a real-time process, aspart of continuous measurement (automatic inspection) of structuresprogressing on a production line, or off line procedure (e.g. as part ofrecipe setup), or a combination of both. The simplest example of the offline mode is by using a single scanning tool image for the recipe setupof an OCD tool. The recipe setup process of such a simple example isschematically illustrated in FIG. 2B.

The process is performed by a modeling system, generally designated 100,of the invention. The system 100 is typically a computer system havinginter alia data input and output utilities 100A, memory utility 100B,and data processing and analyzing utility 100C. The system may include adata presentation utility, such as display. The data input/output may beconfigured for receiving/transmitting data appropriately formatted forwireless communication with other devices. In this specific but notlimiting example, The data processing and analyzing utility 100Cincludes identifier modules ID₁ and ID₂ (software and/or hardwareutilities), a geometrical model creator MC (software and/or hardwareutility), and an optical data generator OMG.

The identifier module ID₁ is preprogrammed for receiving and processinginput image data and identifying unit cell(s); identifier module ID₂ isoperable for processing the unit cell related data (received from moduleID₁) and identifying contours for at least some of the features in theunit cell(s). The final model creator receives the contour-data andanalyzes it to determine and apply a suitable morphological function andthereby create an appropriate physical model.

Thus, the system receives input image data. The latter is obtained by ascanning tool (e.g. SEM) and being indicative of a 2-dimensional image(top view) of a patterned structure or part thereof. The image data maybe entered by user or received from another device (e.g. imaging tool orstorage) directly or via wireless communication. The system mayoptionally be provided with certain design rule data DR including interalia information about the unit cell. The system (its data processingand analyzing utility) is preprogrammed for processing the image data(while utilizing the design rule or not) for automatically identifyingpossible unit cells based on identified repetitions of patternedfeatures (step 20). The case might be such that the system “suggests”several options for the unit cell, and a user chooses the correct option(i.e. a combination of automatic and manual modes for the identificationof unit cell). Then, the system operates for processing image data ofthe unit cell for automatically identifying the contours of all or atleast one of the features within the unit cell, a single such contour Cbeing shown in the figure (step 22), and then operates to extract thecontour related data/image (step 24). The system utilizes the contourrelated data C for determining a 3-dimensional shape 26 of therespective feature (step 28).

Optionally, the user can fine-tune the parameters of the contourdetection algorithm to select a sub-set of features, e.g. in order touse only the contours related to features that reside in a specific(e.g. uppermost) layer of the patterned structure, as opposed to otherfeatures visible in the image.

The so-determined 3-dimensional shape 26 is then further processed forcreating a geometrical model for further generation of the optical modelfor use in interpretation of OCD measurements. This process may includedetermination of a morphological function, which serves for imitating aneffect of changing the feature (e.g. under varying process conditions)and/or a difference between the extracted contour and the real edge ofthe feature. The user may define parameter range(s) for themorphological function to be used for changing (stretching/shrinking)the contour during the calculation. The user may define additionalgeometrical parameters, e.g. structure related parameters such as sidewall angle, underlying layers, etc.

A non-limiting example of the process of stretching/shrinking thecontour (applying the morphological function) for the purposes ofcreation of geometrical model is shown in FIG. 3. In this example, themorphological function acts to stretch the contour C orthogonal to thelocal directions, generally at D, of the normal to the differentsegments of the contour. The local shift of the contour could beaffected by several parameters, such as an overall average shift(overall “scaling” factor or delta-CD), the local direction of thecontour (anisotropy of a parameter change, e.g. different delta-CD_(x)and delta-CD_(y)), the local curvature of the contour (dealingdifferently with internal corners and external corners), etc. In mostcases, the user may define only the range of the general delta-CD, whileother parameters are default or already known through previously setrecipes.

Another example for defining the morphological function may be asfollows: One or more detection algorithms of contour identification maybe applied several times on the same image data, each time withdifferent parameters of the algorithm, e.g. threshold level values, thusobtaining a set of contours. Then, the system, being an expert system(self-learning system) is trained to find the morphological functionusing this set of contours.

Yet another example for finding a suitable morphological function mayutilize multiple images or simulated image data pieces for the patternedstructures or parts thereof having different pattern parameterscorresponding to different process parameters/conditions, e.g. alithographical process performed with different focus and exposureconditions. As a result, a set of contours is obtained from thesemultiple image data pieces, and the system is trained to find themorphological function. It should be noted that in this case if theimages are obtained as a function of multiple process conditions, e.g.as a function of both focus and exposure, then the resultingmorphological function can in turn be a function of thoseparameters/conditions. This property can be later used duringinterpretation of the actual OCD measurements (as part of the fittingprocess) for directly determine the process parameters/conditions fromthe measured data.

Turning back to FIGS. 2A and 2B, the module OMG can operate as followsto generate an appropriate optical model for use in interpretation ofOCD measurements. The module OMG utilizes the geometrical model, basedon the shape of the features defined by the contour, and the currentvalue(s) of the morphological function parameters and additionalparameters as described above. The module OMG is preprogrammed forsimulating a diffraction signature of the determined geometrical modelfor given conditions (measurement system related parameters/conditionsand structure related parameters/conditions), including inter aliaillumination and detection channels' parameters/conditions, includingbut not limited to wavelengths, numerical aperture ofillumination/collection, azimuth and elevation ofillumination/collection, polarization of light, etc. To this end,geometrical model creator MC operates for transforming the shape offeature(s) to a discrete form, including multiple layers and discretevalues for x and y; the transformation can be done using discretizationin the lateral (x, y) directions. The OMG model may utilize any suitableknown method (e.g. RCWA) for diffraction calculation.

The so-obtained optical model can then be used for interpreting actualmeasured data. This process includes a fitting (inverse problem)procedure, either using real-time-regression method or using a librarymethod. The problem parameters include the values of the morphologicalfunction and of the additional parameters, in order to enabledetermination of the structure parameter(s) based on the best matchingcondition (based on the fitted parameters). The overall scaling factor,e.g. delta-CD, can then be correlated to measurements done by othertechniques and/or to process conditions (e.g. exposure), and StatisticalProcess Control (SPC) charts can be used to allow process control.

It should be noted that in some cases more than one different effectcannot be optimally described by one morphological function. It would bein such case an advantage to compose several morphological functionsoperating successively on the contour, e.g. a function describingdifferences between the contour and the actual feature boundaries(correcting errors in the scanning tool and the contour detectionalgorithm) and another function describing the expected changes of thefeature with a change in process parameters. It should also be notedthat morphological transformations referred to herein as “scaling”,although mathematically such transformation may or may not be purescaling (i.e. (x,y)→(ax,ay)); therefore for the purposes of the presentapplication the term “scaling” should be interpreted broader, meaningany transformation function.

As indicated above, the invention may utilize input image data includinga set of multiple images of the structure corresponding to multiple setsof process conditions, e.g. a focus-exposure matrix (FEM), used for thestructure manufacture. In this case, the contour identification isperformed for the entire set of images. In this case, the suitablemorphological function is that which corresponds to continuoustransformation of a certain reference contour to any of the othercontours. The reference contour may for example be that corresponding tothe process parameter or parameters' set, e.g. focus and exposure, inthe middle of the predefined range for said parameter or parameters'set. In addition, scaling can be applied through an additionalmorphological function, as described above. This procedureadvantageously provides that the parameters of the inverse diffractionproblem explicitly include the process parameters. Hence, by performinga fitting of measured signals to the optical model (either by regressionor using a library method), one can directly get not only the measuredshape most fitting to the measured data but also the process parametersmost likely to correspond to the measured data. This kind of informationis specifically useful for process control as there is no need to makeany additional transformation once a correction needs to be applied,i.e. deviation between the standard process parameters and the resultingprocess parameters directly indicate how should the process be tuned toget back to the desired feature shape and profile.

It should be noted that the sensitivity of measurements to the focus andexposure conditions may be enhanced by using specially designed targets.These may for example be targets having many sharp edges, e.g. a2-dimensional array of diamond shapes, which shape is extremelysensitive to focus conditions as the sharp features are printingcorrectly only very close to optimal focus conditions. A structureconsisting of many spaces that are close to the minimum space possiblein a given manufacturing process may be highly sensitive to exposureconditions. Thus, combining information from several different sitescontaining different targets, e.g. one having high sensitivity to focusand the other having high sensitivity to exposure, more accurateinformation about exact full exposure conditions can be provided.

As indicated above, the present invention provides for using image(s)from a scanning tool as part of the measurement process itself (on linemodeling), as well as during setup (off line modeling). The on linemodeling advantageously allows for removing the concern regardingmorphological changes in the contour that are not accommodated by thescaling function and for eliminating the need to fit the scalingfunction over a wide range. If the scaling factor has been accuratelyand reliably characterized during the setup procedure, then by fittingdata in various process conditions, the scaling factor could then beeither fixed or allowed to change in a very small window of uncertainty,thereby reducing the number of floating parameters and simplifying thefitting process during the actual measurements.

In the above-described examples, image data from a scanning tool (e.g.SEM) was used to optimize OCD measurements. As also described above,this procedure may utilize both off line modeling and on line (realtime) modeling, i.e. OCD measurements (real time measurements) are usedfor further optimizing the model created during the initial off linestage. This is illustrated schematically, in a self-explanatory manner,in FIG. 4. As shown, measured SEM image is processed to determine thecontour for each feature in the unit cell and crate a geometrical model(3-shape of at least some features of the unit cell). This data is usedfor generating optical model for the OCD measurements. Independently,OCD measured data is provided in real time, and interpreted bycomparison with the optical model data (fitting procedure), resulting indetermination of optimal structure parameters (e.g. height, SWA, etc.).These parameters are output, e.g. to user for the purposes of processcontrol. Concurrently, these parameters are used to optimize thegeometrical model and accordingly further optimize the optical model.

The invention, in its yet other aspect, provides for combining data fromtwo measurement tools of different types (operating on differentphysical principles), i.e. “imaging” and “non-imaging” types, such asrespectively, CD-SEM and OCD. This is exemplified schematically in FIGS.5 and 6. As shown in the figures, SEM and OCD tools operateindependently to provide measured data pieces indicative of respectivelycritical dimensions (CD_(SEM)) and wide-band width (WB), and CD_(ocD)and side wall angle (SWA). These parameters are input into a modeling(computing) system 200. The system 200 may be a computer systemincluding inter alia data input and output utilities, memory utility,and data processing and analyzing utility, as well as data presentationutility (e.g. display). The data processing and analyzing utility inthis case is configured and operable to process the input data anddetermine correlation curves for the critical dimensions measured by SEMand OCD tools, i.e. Optical-CD and SEM-CD, and for SB and SWAparameters, i.e. OCD-angle and SEM-WB. By this, correlation curves orcorrelation functions are determined, and correlation parameters, mainlyslope and offset, are obtained. For example, CD values found by OCD canbe correlated to CD values found by CD-SEM and Side Wall Angle valuesfound by OCD can be correlated by White-Band width values found byCD-SEM. In order to verify correct correlation coefficients, it is bestpractice to perform the setup using a set of measurements representing awide range of process conditions, e.g. for lithography process aFocus/Exposure matrix. In the example of FIG. 5, the correlationparameters are found off line, and can then be used for optimizinginterpretation of OCD measurements, e.g. in case the parameter ofinterest, such as SWA of photoresist, is a weak parameter (i.e.parameter that affects the measured data, i.e. OCD spectra, less thatother “strong” parameters.

FIG. 6 shows how the correlation parameters obtained during the setupstage (FIG. 5) are used to optimize the OCD measurements, i.e. analysisof OCD measured data. As shown, correlation data is used to determinecorrelated or adjusted value of CD_(SEM) and WB. These adjustedparameters are input into a control unit of the OCD station and used foroptimizing the calculation of CD and SWA parameters.

Thus, during mass production of patterned structures such assemiconductor wafers, measurements can be taken from some or all of thesites in the structure by both OCD and SEM tools, and the measured dataof one tool (e.g. the CD-SEM) can be adjusted by using correlationcurves, and then the adjusted values can be used for the datainterpretation process of the other tool (e.g. the OCD). By performingthe above correlation and adjustment, the number of floating parametersin the second measurement can be reduced, thus enabling more stablemeasurement of the remaining parameters, e.g. “weak” parameters.

It should be noted that in order to reduce noise in the first measureddata and thus reduce its effect on the interpretation of the secondmeasured data, a so-called “soft injection” method can be used. This canbe performed as follows: The second measurement (e.g. by OCD tool) isfirst performed with no prior knowledge based on the first measurement,e.g. CD-SEM. Then, an error function that might exist in the secondmeasurement is reduced using a penalty function concept. This techniquemay be similar to that described in the International patent applicationNo. PCT/IL2011/000188, assigned to the assignee of the presentapplication, which application is incorporated herein by reference. Morespecifically, a penalty function is determined and added to the errorfunction of the optimization process, favoring the measurement resultsto be similar to the (adjusted) values obtained from the first (CD-SEM)measurement. Such a penalty function may be for example proportional tothe squared difference between the two measurements. The optimizationprocess continues until a certain convergence condition is achieved,using a target function that includes both the original error functionand the penalty function. This process advantageously provides fortuning the “strength” of the penalty function, thus reducing theamplification of noise in the first measurement on the final result.

The present invention, in its yet further aspect, provides for jointlyoptimizing data interpretation models of measurement tools of differenttypes using measured data from both of these tools. This is illustratedschematically in FIG. 7. In this case, a combined model including boththe SEM and OCD measurements is created. The process starts from acommon “candidate profile”, being certain theoretical data (knowledge,prediction) about the profile of a patterned structure, e.g. at leastone feature of unit cell. The candidate profile is used for simulatingthe OCD data (optical response) and the SEM data (image) based oncorresponding predefined models for data analysis. The so-obtainedsimulated OCD and SEM data are compared (by fitting) to correspondingmeasured data and an error is (residual error, Merit Function)determined for each measurement. The two residual errors are combined toa total error, which is minimized in a profile optimization process byiteratively adjusting the candidate profile. The profile correspondingto a minimal total error is selected to be output to the process tool orfab host, as a measurement of the profile and used for process control.

Thus, in this case, both measurement techniques (e.g. OCD and SEM) areassumed to utilize model based interpretation. The optimization is doneconcurrently for both measurement tools by assuming at each step of theiterative optimization process a single geometrical profile (3-Dstructure) and simulating the expected response for each of the toolsusing its own physical model. The simulated data are then compared tothe measured data yielding error functions for each of the tools. Theseparate error functions are combined into a single Total Error figure.The optimization process is then acting to minimize the Total Errorthrough modifying the parameters of the common geometrical profile untilconvergence. By combining the two (or possibly any number) channels inthis way, the information that resides in each of the measurements canbe fully utilized without a need to extract the results from the realmof a single physical interaction operating in one measurement tool andinto the realm of another physical interaction acting in the other tool,thus avoiding potential “translation” problems.

It should be understood that running the combined interpretation using alarge or sufficiently diversified set of examples enables optimizationof the specific models. In the case of OCD, the model can be optimizedtowards a correct setting of fixed parameters, either geometric ofparameters related to the optical properties of materials involved inthe structure. In the case of SEM model, using the additionalinformation obtained through the above process the model can be tunedfor example with respect to the efficiency of extraction of secondaryelectrons from different depths, different materials, geometries, etc.

The present invention provides for combining measurements of differenttools (different types of measurements) while performing themeasurements on different sites of the structure. Such combination canbe beneficial for various reasons. For example, it allows for increasingthe overall sampling across a given wafer, as well as allows forsampling different wafers in a lot, and for measuring both inside thedie, on a device, and in the scribe line on a test pattern while linkingthe two.

In order to be able to utilize measurements coming from differentlocations an additional element may be used, i.e. a model for thebehavior of parameters across the wafer/die/lot. Once a model isdefined, a link can be created between different measurements bypenalizing results that are far away from the model (similar to the“soft injection” explained above) and the full data set, includingmeasurements done on different locations can be re-analyzed. The wholeprocess may be repeated until the full data set converges to minimum(stable results). Through this process information can flow betweenmeasurements done on different locations, enabling the benefitsexplained above.

1. A system for use in inspection of patterned structures, the systemcomprising: data input utility for receiving first type of dataindicative of image data on at least a part of the patterned structure,and data processing and analyzing utility configured and operable foranalyzing the image data, and determining a geometrical model for atleast one feature of a pattern in said structure, and using saidgeometrical model for determining an optical model for second type ofdata indicative of optical measurements on a patterned structure.
 2. Thesystem of claim 1, wherein said data processing and analyzing utilitycomprises an identifier utility configured and operable for processingdata indicative of said image data and determining a contour for atleast one feature of the pattern, and a geometrical model creatorutility connected to said identifier utility and operable for thedetermination of the geometrical model.
 3. The system of claim 1,wherein said data processing and analyzing utility comprises anidentifier utility configured and operable for processing the image dataand identifying at least one unit cell comprising said at least onefeature of the pattern, and generating said data to the contouridentifier utility.
 4. The system of claim 1, comprising a memoryutility.
 5. The system of claim 4, wherein the memory utility serves forstoring certain design rule data indicative of at least one feature of apattern in said structure.
 6. The system of claim 1, wherein said dataprocessing and analyzing utility is configured and operable forreceiving measured data of said second type and processing it foroptimizing the first image data.
 7. The system of claim 1, wherein theimage data is measured data obtained by a scanning tool.
 8. The systemof claim 7, wherein said scanning tool includes at least one of SEM andAFM tool.
 9. The system of claim 1, wherein said second type of datacorresponds to measured data obtainable by a scatterometer.
 10. Ameasurement system comprising at least one measurement tool forobtaining measured data of at least one of the first and second types,and said system of any one of the preceding claims configured forcommunicating with said at least one measurement tool.
 11. Ascatterometry system comprising a measurement tool configured andoperable for measuring on patterned structures and generating opticaldata of a second type, and said system according to claim
 1. 12. Amethod for use in inspection of patterned structures, the methodcomprising: receiving first type of data indicative of image data on atleast a part of the patterned structure, processing and analyzing dataindicative of the image data and determining a geometrical model for atleast one feature of a pattern in said structure, using said geometricalmodel for determining an optical model for second type of dataindicative of optical measurements on a patterned structure.
 13. Themethod of claim 12, wherein said determining of the geometrical modelcomprises processing and analyzing data indicative of the received imagedata and determining a contour for at least one feature of the pattern,and processing said at least one contour for the determination of thegeometrical model.
 14. The method of claim 12, comprising processing andanalyzing the received image data and identifying at least one unit cellcomprising said at least one feature of the pattern.
 15. The method ofclaim 12, comprising providing certain design rule data indicative of atleast one feature of a pattern in said structure.
 16. The method ofclaim 12, comprising receiving measured data of said second type andprocessing it for optimizing the first image data.
 17. The method ofclaim 12, wherein the image data is measured data obtained by a scanningtool.
 18. The method of claim 17, wherein said scanning tool includes atleast one of SEM and AFM tool.
 19. The method of claim 12, wherein saidsecond type of data corresponds to measured data obtainable by ascatterometer.
 20. The method of claim 12, wherein said patternedstructures are semiconductor wafers.
 21. A method for use in inspectionof patterned structures, the method comprising: receiving image dataindicative of one or more images of at least a part of the patternedstructure obtained by a scanning tool, processing and analyzing dataindicative of said image data and determining a geometrical model for atleast one feature of a pattern in said structure, using said geometricalmodel for determining an optical model for scatterometry based opticalmeasurements on a patterned structure, thereby enabling use of saidgeometrical model for interpreting scatterometry based measurementsapplied to the patterned structure progressing on a production line.